• The Journal of the Korea Contents Association
• /
• v.19 no.2
• /
• pp.581-590
• /
• 2019

As the film industry develops in China, movie posters come in various types. Among them, 'concept poster' has been recognized as an important means to form the first impression of a movie at the early stage of promoting the movie. In China, however, there are not many movie posters that have been recognized for their creativity. Accordingly, it calls for research on creative methods of implicitly expressing the content of the movie. This study analyzed visual expressions in the concept posters of HuangHai, who has been recognized not only for commerciality but also for artistry. The results showed that he did not use images of famous scenes and actors in the movie. Instead, he created the implicit image that reflects the main theme of the movie by using Minimalism, color, typography, and pictorial images. This study has a significance in terms of providing fundamental resources for improving movie poster designs in China.

• Journal of Digital Convergence
• /
• v.17 no.1
• /
• pp.123-130
• /
• 2019

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.25-44
• /
• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• The Journal of the Korea Contents Association
• /
• v.11 no.1
• /
• pp.429-437
• /
• 2011

Movie network as a social network shows power-law distribution that is one of distinct properties in scale-free network. We constructed movie network from 799 Korea movies that screened from 2000 to 2009 and analyzed structural properties of the network. The 799 movies was classified three groups as a spectator mobilizing power. One million spectators mobilizing power movie was denoted the first class. The best 10 movie directors who produced at least three movies for ten years and had 70% the first class movie of them were selected. We also preferred the best 20 movie actors who played at least five movies for ten years and had 70% the first class movie of them. We re-constructed core movie network that composed the best 10 directors, the best 20 movie stars, and 157 movies that were produced by the directors or were played by the movie stars. We predict a possible combination of the director and movie actor as a category of the movie that has highly spectator mobilizing power. Here, we provide insight and method for producing high spectators mobilizing power movies

• Archives of design research
• /
• v.21
• /
• pp.233-243
• /
• 1997

In 1970s, Computer Graphics of still and geometry changed computer Animation of image, and Computer Animation has diversely been used in movie, TV, fashion, sports, education, basic science, medical science, etc. by the development of LSI technology and the large size of computer in 1980s. Since Computer Animation was first used by movie of Futureworld in 1973, we easily experienced the essence of Computer Animation made of the Little Mermaid. Beauty and the Beast, the Lion King, Aladdin, etc. in Disney Animation and Terminator. Jurassic Park, the Mask, etc. in movie. And in other countries that have got the diversely special effect and knowhow in technology are effectively using the Computer Animation now. What situation we Korea are in now, if we compare the Computer Animation with that of other country using the progressive movie\ulcorner Although we first producted the movie title of Ticket, 10years ago, we have rarely been used it in movie, yet. Therefore, we know that it is very important for us to examine the historical and technical side for the purpose of overcoming the technological gap.

• Journal of Information Technology Applications and Management
• /
• v.26 no.3
• /
• pp.19-27
• /
• 2019

In this study, we propose a methodology for predicting the movie audience based on movie information that can be easily acquired before opening and effectively distinguishing qualitative variables. In addition, we constructed a model to estimate the number of movie audiences at the time of data acquisition through the configured variables. Another purpose of this study is to provide a criterion for categorizing success of movies with qualitative characteristics. As an evaluation criterion, we used information gain ratio which is the node selection criterion of C4.5 algorithm. Through the procedure we have selected 416 movie data features. As a result of the multiple linear regression model, the performance of the regression model using the variables selection method based on the information gain ratio was excellent.

• Proceedings of the Korean Society for Quality Management Conference
• /
• 2006.04a
• /
• pp.95-99
• /
• 2006

Resulting from a development of Korea's film industry, the number of the audience who visits a movie theater is increasing. so, interest in indoor service which is provided in the movie theater is also increasing, and there are needs for raising a movie theater's competitive power and an endeavor to offer a higher standard of service to customer. thus, this study will define service quality factor of a movie theater and provide ways of improving service quality by analyzing the effect of service quality on customer satisfaction and re-visit intention.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.496-499
• /
• 2019

The study aims to collect detailed movie information from box office of the Korea Film Council and data on Naver's movie ratings to analyze important factors affecting the movie's popularity based on movie audiences and ratings.

• Asia Marketing Journal
• /
• v.21 no.1
• /
• pp.1-21
• /
• 2019

Online platforms often provide rating information to customers to relieve the uncertainty they encounter when purchasing experience goods. Prior research has focused mostly on the roles of rating volume and the valence of an average rating among the various possibilities. However, less frequently investigated is the effect of rating dispersion, which may be associated with uncertainty regarding how well a product fits a customer's personal preference, on new trials of experience goods. In this study, we examine the effect of rating dispersion on new trials of experience goods and identify the conditions which intensify or reduce the effect. Empirical analyses of movie box office sales data and online rating data reveal three interesting findings. First, movie sales decrease as movie ratings become increasingly dispersed. Second, the negative effect of rating dispersion on movie sales is more pronounced with more rating volume. Third, this negative effect weakens when additional information about a movie is available (i.e., higher average rating, greater star power, and time since its release). We discuss the academic and practical implications of our findings.

• Journal of Digital Convergence
• /
• v.13 no.11
• /
• pp.365-371
• /
• 2015

This experimental research explores the effect of the movie that can be classified as non-news media on viewers' political and social attitude on the theoretical basis of priming effect, focused on the movie 'cart' that deals with non-regular workers' labor movement. For this experimental research, one group of subjects composed of university students were exposed to movie 'cart', and the other not to the movie. After watching a movie, each group of subjects responded to questions that are designed to measure the seriousness of non-regular workers' problem, the necessity of labor movement, support of opposition party, support of the current government. This research found that subjects exposed to the movie 'cart' consider non-regular workers' problem more serious and supports opposition party more positively than subjects not exposed to movie 'cart.' However hypotheses that expected the positive support of treated subjects on labor union and less positive support of treated subjects on current government were rejected.